Method for determining proximity of at least one object using electronic device

ABSTRACT

A method for determining a proximity of at least one object using an electronic device is provided. The method includes obtaining hover data from a touch panel of the electronic device. Further, the method includes determining at least one of a first hover or a second hover based on the hover data of the at least one object obtained for the touch panel. Further, the method includes determining the proximity of the at least one object to the touch panel of the electronic device based on at least one of the first hover or the second hover.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is based on and claims priority under 35 U.S.C. §119(a) of an Indian patent application number 201941034130, filed onAug. 23, 2019, in the Indian Intellectual Property Office, thedisclosure of which is incorporated by reference herein in its entirety.

BACKGROUND 1. Field

The disclosure relates to a method for determining a proximity of atleast one object using an electronic device.

2. Description of Related Art

Many types of input devices are presently available for performingoperations in a computing system, such as buttons or keys, mice,trackballs, joysticks, touch sensor panels, touch screens and a like.Touch screens, in particular, are becoming increasingly popular becauseof their ease and versatility of operation as well as their decliningprice. The touch screen can include a transparent touch sensor panelpositioned in front of a display device, such as a liquid crystaldisplay (LCD), or an integrated touch screen in which touch sensingcircuitry is partially or fully integrated into the display, and thelike. Touch screens can allow a user to perform various functions bytouching the touch screen using a finger, stylus or other objects at alocation that may be dictated by a user interface (UI) being displayedby the display device. In general, touch screens can recognize a touchevent and the position of the touch event on the touch sensor panel, andthe computing system can then interpret the touch event in accordancewith the display appearing at the time of the touch event, andthereafter can perform one or more actions based on the touch event.

While some touch sensors and a proximity sensor can also detect a hoverevent, i.e., an object near but not touching the touch sensor, typicalhover detection information may be of limited practical use due to, forexample, limited hover detection range, an inefficient gathering ofhover information, and the like.

In an existing system, an electronic device needs a proximity hardwaresensor to detect the hover event and because of the proximity hardwaresensor production cost of the electronic device increases. Further, theproximity hardware sensor takes a lot of space in the front screendisplay of the electronic device. Further, to create a fully bezel-lessscreen it is required to find an alternative solution to replace theproximity hardware sensor. A solution is required which performs all thefunction of proximity without increasing any other hardware component.So, the proposed system and method focuses on the detection of the hoverevent without using the proximity hardware sensor.

Thus, it is desired to address the above mentioned disadvantages orother shortcomings or at least provide a useful alternative.

The above information is presented as background information only toassist with an understanding of the disclosure. No determination hasbeen made, and no assertion is made, as to whether any of the abovemight be applicable as prior art with regard to the disclosure.

SUMMARY

Aspects of the disclosure are to address at least the above-mentionedproblems and/or disadvantages and to provide at least the advantagesdescribed below. Accordingly, an aspect of the disclosure is to providea method and system for determining a proximity of at least one objectusing an electronic device.

Another aspect of the disclosure is to obtain hover data from a touchpanel of the electronic device.

Another aspect of the disclosure is to determine at least one of a firsthover or a second hover based on the hover data of the at least oneobject obtained for the touch panel.

Another aspect of the disclosure is to determine the proximity of the atleast one object to the touch panel of the electronic device based on atleast one of the first hover or the second hover.

Additional aspects will be set forth in part in the description whichfollows and, in part, will be apparent from the description, or may belearned by practice of the presented embodiments.

In accordance with an aspect of the disclosure a method for determininga proximity of at least one object using an electronic device isprovided. The method includes obtaining hover data from a touch panel ofthe electronic device. Further, the method includes determining at leastone of a first hover or a second hover based on the hover data of the atleast one object obtained for the touch panel. Further, the methodincludes determining the proximity of the at least one object to thetouch panel of the electronic device based on at least one of the firsthover or the second hover.

In accordance with an aspect of the disclosure, the first hover is atouch panel calculation-based hover and the second hover is a machinelearning (ML)-based hover.

In accordance with an aspect of the disclosure, the hover data isobtained based on at least one of sensor comprising a capacitive sensor,a resistive sensor, an inductive sensor, an ultrasonic sensor, and aluminance sensor.

In accordance with an aspect of the disclosure, the hover data isobtained based on a change in capacitance of column and row electrodesof the touch panel while the hover of the at least one object isdetected over the touch panel of the electronic device.

In accordance with an aspect of the disclosure, the column and rowelectrodes of the touch panel are automatically configured by detectingthe at least one application running in the electronic device,determining, by the electronic device, the column and row electrodes ofa row-and-column electrode matrix of the touch screen based on the atleast one application running in the electronic device, andautomatically configuring the column and row electrodes to obtain thehover data of the at least one object.

In accordance with an aspect of the disclosure, determining the firsthover based on the hover data of the at least one object obtained forthe touch panel includes obtaining the hover data at a first time,obtaining the hover data at a second time, determining a hover datadifference between the hover data obtained at the first time with thehover data obtained at the second time, determining whether the hoverdata difference meets a capacitance threshold, and determining the firsthover when the hover data difference meets the capacitance threshold.

In accordance with an aspect of the disclosure, the second hover isdetermined by using a trained probabilistic touch screen panel (TSP)hover ML model of the touch panel based on the obtained the hover dataover a plurality of time intervals.

In accordance with an aspect of the disclosure, determining theproximity of the at least one object to the touch panel of theelectronic device based on the first hover and the second hover includesassigning a first dynamic weight to the first hover, assigning a seconddynamic weight to the second hover, determining a candidate hover basedon the first hover, the first dynamic weight assigned to the firsthover, the second hover, and the second dynamic weight assigned to thesecond hover, determining whether the candidate hover meets a hoverthreshold, and detecting one of the at least one object is in theproximity to the touch panel of the electronic device when the candidatehover meets the hover threshold and the at least one object is not inthe proximity to the touch panel of the electronic device when thecandidate hover does not meet the hover threshold.

In accordance with another aspect of the disclosure, an electronicdevice for determining a proximity of at least one object using theelectronic device is provided. The electronic device includes a memoryand at least one processor operationally coupled to the memory. The atleast one processor is configured to obtain hover data from a touchpanel of the electronic device. Further, the at least one processor isconfigured to determine at least one of a first hover and a second hoverbased on the hover data of the at least one object obtained for thetouch panel. Further, the at least one processor is configured todetermine the proximity of the at least one object to the touch panel ofthe electronic device based on at least one of the first hover and thesecond hover.

Other aspects, advantages and salient features of the disclosure willbecome apparent to those skilled in the art from the followingdescription, which, taken in conjunction with the annexed drawings,discloses various embodiments of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects, features, and advantages of certainembodiments of the disclosure will be more apparent from the followingdescription taken in conjunction with the accompanying drawings, inwhich:

FIG. 1 illustrates a block diagram of an electronic device fordetermining a proximity of at least one object while using at least oneapplication in the electronic device according to an embodiment of thedisclosure;

FIG. 2 is a flow diagram illustrating a method for determining aproximity of at least one object using an electronic device according toan embodiment of the disclosure;

FIG. 3A is a flow diagram illustrating a method for automaticconfiguration of column and row electrodes of a touch panel according toan embodiment of disclosure;

FIG. 3B is a flow diagram illustrating a method for determining a firsthover based on a hover data of at least one object obtained for a touchpanel according to an embodiment of disclosure;

FIG. 3C illustrates a setting range and a threshold difference value ofcolumn and row electrodes of a touch panel for different modes accordingto an embodiment of disclosure;

FIGS. 3D and 3E illustrate setting range and threshold difference valueof column and row electrodes of a touch panel after experimenting withhardware proximity sensor data according to various embodiments ofdisclosure;

FIG. 3F illustrates a scenario of fetching hover data and calculatingdifference between column and row electrodes values to determine a firsthover value according to an embodiment of disclosure;

FIG. 4 illustrates a second hover determined by using a trainedprobabilistic touch screen panel (TSP) hover machine learning (ML) modelof a touch panel based on an obtained hover data over a plurality oftime intervals according to an embodiment of disclosure;

FIG. 5 is a flow diagram illustrating a method for determining aproximity of at least one object to a touch panel of an electronicdevice based on a first hover and a second hover according to anembodiment disclosure;

FIG. 6 is a flow diagram illustrating a method for determining aproximity for an In call mode and a 3^(rd) party application modeaccording to an embodiment disclosure;

FIG. 7 is a flow diagram illustrating a method for determining aproximity for a pocket mode according to an embodiment of disclosure;

FIGS. 8A and 8B illustrate proximity determination when a call isreceived and an electronic device is far/near from a user of theelectronic device according to various embodiments of disclosure; and

FIGS. 9A and 9B illustrate proximity determination when a hover isperformed on second/first half of an electronic device according tovarious embodiments of the disclosure.

The same reference numerals are used to represent the same elementsthroughout the drawings.

DETAILED DESCRIPTION

The following description with reference to the accompanying drawings isprovided to assist in a comprehensive understanding of variousembodiments of the disclosure as defined by the claims and theirequivalents. It includes various specific details to assist in thatunderstanding but these are to be regarded as merely exemplary.Accordingly, those of ordinary skill in the art will recognize thatvarious changes and modifications of the various embodiments describedherein can be made without departing from the scope and spirit of thedisclosure. In addition, descriptions of well-known functions andconstructions may be omitted for clarity and conciseness.

The terms and words used in the following description and claims are notlimited to the bibliographical meanings, but, are merely used by theinventor to enable a clear and consistent understanding of thedisclosure. Accordingly, it should be apparent to those skilled in theart that the following description of various embodiments of thedisclosure is provided for illustration purpose only and not for thepurpose of limiting the disclosure as defined by the appended claims andtheir equivalents.

It is to be understood that the singular forms “a,” “an,” and “the”include plural referents unless the context clearly dictates otherwise.Thus, for example, reference to “a component surface” includes referenceto one or more of such surfaces.

Descriptions of well-known components and processing techniques areomitted so as to not unnecessarily obscure the embodiments herein. Inaddition, the various embodiments described herein are not necessarilymutually exclusive, as some embodiments can be combined with one or moreother embodiments to form new embodiments. The term “or” as used herein,refers to a non-exclusive or, unless otherwise indicated. The examplesused herein are intended merely to facilitate an understanding of waysin which the embodiments herein can be practiced and to further enablethose skilled in the art to practice the embodiments herein.Accordingly, the examples should not be construed as limiting the scopeof the embodiments herein.

As is traditional in the field, embodiments may be described andillustrated in terms of blocks which carry out a described function orfunctions. These blocks, which may be referred to herein as units ormodules or the like, are physically implemented by analog or digitalcircuits, such as logic gates, integrated circuits, microprocessors,microcontrollers, memory circuits, passive electronic components, activeelectronic components, optical components, hardwired circuits, or thelike, and may optionally be driven by firmware and software. Thecircuits may, for example, be embodied in one or more semiconductorchips, or on substrate supports, such as printed circuit boards and thelike. The circuits constituting a block may be implemented by dedicatedhardware, or by a processor (e.g., one or more programmedmicroprocessors and associated circuitry), or by a combination ofdedicated hardware to perform some functions of the block and aprocessor to perform other functions of the block. Each block of theembodiments may be physically separated into two or more interacting anddiscrete blocks without departing from the scope of the disclosure.Likewise, the blocks of the embodiments may be physically combined intomore complex blocks without departing from the scope of the disclosure.

The accompanying drawings are used to help easily understand varioustechnical features and it should be understood that the embodimentspresented herein are not limited by the accompanying drawings. As such,the disclosure should be construed to extend to any alterations,equivalents and substitutes in addition to those which are particularlyset out in the accompanying drawings. Although the terms first, second,and the like, may be used herein to describe various elements, theseelements should not be limited by these terms. These terms are generallyonly used to distinguish one element from another.

Accordingly, embodiments herein achieve a method for determining aproximity of at least one object using an electronic device. The methodincludes obtaining hover data from a touch panel (i.e., a touch screenpanel (TSP)) of the electronic device. Further, the method includesdetermining at least one of a first hover and a second hover based onthe hover data of the at least one object obtained for the touch panel.Further, the method includes determining the proximity of the at leastone object to the touch panel of the electronic device based on at leastone of the first hover and the second hover.

Referring now to the drawings, and more particularly to FIGS. 1 through9B, there are shown preferred embodiments.

FIG. 1 illustrates a block diagram of an electronic device fordetermining a proximity of at least one object while using at least oneapplication in the electronic device according to an embodiment of thedisclosure.

Referring to FIG. 1, an electronic device (100) can be, for example, butnot limited to a smartphone, a laptop, a desktop, a smartwatch, a smartTV or a like.

In an embodiment of the disclosure, the electronic device (100) includesa memory (110), a processor (120), a communicator (130), a display(140), and a touch panel (140 a).

The memory (110) also stores instructions to be executed by theprocessor (120). The memory (110) may include non-volatile storageelements. Examples of such non-volatile storage elements may includemagnetic hard discs, optical discs, floppy discs, flash memories, orforms of electrically programmable memories (EPROM) or electricallyerasable and programmable (EEPROM) memories. In addition, the memory(110) may, in some examples, be considered a non-transitory storagemedium. The term “non-transitory” may indicate that the storage mediumis not embodied in a carrier wave or a propagated signal. However, theterm “non-transitory” should not be interpreted that the memory (110) isnon-movable. In some examples, the memory (110) can be configured tostore larger amounts of information than the memory. In certainexamples, a non-transitory storage medium may store data that can, overtime, change (e.g., in Random Access Memory (RAM) or cache). In anembodiment of the disclosure, the memory (110) can be an internalstorage unit or it can be an external storage unit of the electronicdevice (100), a cloud storage, or any other type of external storage.

The memory (110) includes a hover database (110 a) and an applicationrepository (110 b). The hover database (110 a) is configured to storehover data from a touch panel (140 a) of the electronic device (100)while using the at least one application in the electronic device (100).The hover data is at least one of the first hover and second hovervalues (e.g., RX and TX values). RX provides the sum of capacitance ofrow electrode and TX provides the sum of the capacitance of columnelectrode.

The application repository (110 b) is configured to store hover data ofdifferent application of the electronic device (100), the applicationrepository (110 ba to 110 bn) can be, for example, but not limited tocalling application, gallery application, camera application, gamingapplication, business application, education application, lifestyleapplication, entertainment application, utility application, travelapplication, health, and fitness application.

The processor (120) communicates with the memory (110), the communicator(130), and the display (140). The processor (120) is configured toexecute instructions stored in the memory (110) and to perform variousprocesses. In an embodiment of the disclosure, the processor (120)includes a touch panel calculation-based hover engine (120 a), anML-based hover engine (120 b), a proximity determiner (120 c), a columnand row electrodes configure engine (120 d).

The processor (120) is configured to obtain hover data from a touchpanel (140 a) of the electronic device (100) using the electronic device(100). Further, the processor (120) is configured to determine at leastone of a first hover and a second hover based on the hover data of theat least one object obtained for the touch panel (140 a). In anembodiment of the disclosure, the hover data is obtained based on atleast one of sensor comprising a capacitive sensor, a resistive sensor,an inductive sensor, an ultrasonic sensor, and a luminance sensor.

Further, the processor (120) is configured to determine the proximity ofthe at least one object to the touch panel (140 a) of the electronicdevice (100) based on at least one of the first hover and the secondhover.

In an embodiment of the disclosure, the first hover is a touch panelcalculation-based hover (i.e., HV1) and the second hover is a machinelearning (ML)-based hover (i.e., HV2).

In an embodiment of the disclosure, the hover data is obtained based ona change in capacitance of column (i.e., TX) and row (i.e., RX)electrodes of the touch panel (140 a) while the hover of the at leastone object is detected over the touch panel (140 a) of the electronicdevice (100). In other embodiments of the disclosure, the hover data isobtained based on a change in resistance when the sensor used in thetouch panel is resistive sensor. In yet another embodiment of thedisclosure, the hover data is obtained based on a change in inductancewhen the sensor used in the touch panel is inductive sensor. In yetanother embodiment of the disclosure, the hover data is obtained basedon a change in ultrasonic when the sensor used in the touch panel isultrasonic sensor. In yet another embodiment of the disclosure, thehover data is obtained based on a change in luminance when the sensorused in the touch panel is luminance sensor.

The touch panel calculation-based hover engine (120 a) is configured todetermine the first hover based on the hover data of the at least oneobject obtained for the touch panel (140 a). Further, the touch panelcalculation-based hover engine (120 a) obtains the hover data at a firsttime, obtains the hover data at a second time, determines a hover datadifference (i.e., RXDiff and TXDiff) between the hover data obtained atthe first time with the hover data obtained at the second time,determines whether the hover data difference meets an electrodethreshold (i.e., RX_Threshold and TX_Threshold), and determines thefirst hover when the hover data difference meets the electrode threshold(i.e., RXDiff greater than RX_Threshold and TXDiff greater thanTX_Threshold).

The ML-based hover engine (120 b) determines the second hover by using atrained probabilistic touch screen panel (TSP) hover ML model of thetouch panel (140 a) based on the obtained the hover data over aplurality of time intervals.

The proximity determiner (120 c) is configured to determine theproximity of the at least one object to the touch panel (140 a) of theelectronic device (100) based on the first hover and the second hover.Further, the proximity determiner (120 c) assigns a first dynamic weight(i.e., coefficient A) to the first hover, assigns a second dynamicweight (i.e., coefficient B) to the second hover, determines a candidatehover (i.e., HV) based on the first hover, the first dynamic weightassigned to the first hover, the second hover, and the second dynamicweight assigned to the second hover, determines whether the candidatehover meets a hover threshold (i.e., 0.65), and detects one of the atleast one object is in the proximity to the touch panel (140 a) of theelectronic device (100) when the candidate hover meets the hoverthreshold and the at least one object is not in the proximity to thetouch panel (140 a) of the electronic device (100) when the candidatehover does not meet the hover threshold.

The column and row electrodes configure engine (120 d) is configure thecolumn and row electrodes of the touch panel (140 a) automatically.Further, the column and row electrodes configure engine (120 d) detectsthe at least one application running (i.e., call mode/pocket mode/3^(rd)party application mode) in the electronic device (100), determines thecolumn and row electrodes of a row-and-column electrode matrix of thetouch screen (i.e., the display (140)) based on the at least oneapplication running in the electronic device (100), and automaticallyconfigure the column and row electrodes to obtain the hover data of theat least one object.

The communicator (130) is configured for communicating internallybetween internal hardware components and with external devices via oneor more networks.

Although the FIG. 1 shows various hardware components of the electronicdevice (100) but it is to be understood that other embodiments are notlimited thereon. In other embodiments of the disclosure, the electronicdevice (100) may include less or more number of components. Further, thelabels or names of the components are used only for illustrative purposeand does not limit the scope of the disclosure. One or more componentscan be combined together to perform the same or substantially similarfunction to determine the proximity of at least one object while usingat least one application in the electronic device (100).

FIG. 2 is a flow diagram 200 illustrating a method for determining aproximity of at least one object using an electronic device according toan embodiment of the disclosure.

Referring to FIG. 2, the operations (202-206) are performed by theelectronic device (100).

At operation 202, the method includes obtaining hover data from thetouch panel (140 a) of the electronic device (100). At operation 204,the method includes determining at least one of the first hover and thesecond hover based on the hover data of the at least one object obtainedfor the touch panel (140 a). At operation 206, the method includesdetermining the proximity of the at least one object to the touch panel(140 a) of the electronic device (100) based on at least one of thefirst hover and the second hover.

FIG. 3A is a flow diagram 300A illustrating a method for automaticconfiguration of column and row electrodes of the touch panel accordingto an embodiment of the disclosure.

Referring to FIG. 3A, the operations (302 a-306 a) are performed by thecolumn and row electrodes configure engine (120 d).

At operation 302 a, the method includes detecting the at least oneapplication running in the electronic device (100). At operation 304 a,the method includes determining the column and row electrodes of arow-and-column electrode matrix of the touch screen based on the atleast one application running in the electronic device (100). Atoperation 306 a, the method includes automatically configuring thecolumn and row electrodes to obtain the hover data of the at least oneobject.

FIG. 3B is a flow diagram 300B illustrating a method for determining afirst hover based on a hover data of at least one object obtained for atouch panel) according to an embodiment of the disclosure.

Referring to FIG. 3B, the operations (302 b-310 b) are performed by thetouch panel calculation-based hover engine (120 a).

At operation 302 b, the method includes obtaining the hover data at thefirst time. At operation 304 b, the method includes obtaining the hoverdata at the second time. At operation 306 b, the method includesdetermining the hover data difference between the hover data obtained atthe first time with the hover data obtained at the second time. Atoperation 308 b, the method includes determining whether the hover datadifference meets the capacitance threshold. At operation 310 b, themethod includes determining the first hover when the hover datadifference meets the capacitance threshold.

FIG. 3C illustrates setting range and threshold difference value ofcolumn and row electrodes of a touch panel for different modes accordingto an embodiment of the disclosure.

Referring to FIG. 3C, the notation “a” indicates that the RX and TXrange for the electronic device (100). The RX range is 1 to 32 and theTX range is 33 to 47. The notation “b” indicates that different range ofRX and TX to find out the best area of TSP on which hover data valuesshould be observed to predict proximity for different modes (i.e., callmode/pocket mode/3^(rd) party application mode). The notation “c”indicates that the threshold of RX and TX difference while calculatingproximity in the proposed solution, a difference of consecutive RX andTX values are taken for different modes. The threshold is set todetermine if the difference between various RX and TX values changesproximity/hover state or not.

FIGS. 3D and 3E illustrate setting range and threshold difference valueof column and row electrodes of a touch panel after experimenting withhardware proximity sensor data according to various embodiments of thedisclosure.

Referring to FIGS. 3D and 3E, the notation “a” indicates that when theelectronic device (100) held in hand normally, the TX values of endsincreases. For this scenario range of the RX is 1 to 32 and range of theTX is 33 to 47. In result, the proposed solution detects proximity whilefor the same scenario the existing system with hardware proximity sensordetects no proximity. Because of wrong detection of the proximity theproposed method has to change in RX range from 27 to 29.

The notation “b” indicates that when hover done to bottom half of theelectronic device (100). For this scenario range of the RX is 1 to 32and range of the TX is 37 to 41. In result, the proposed solutiondetects proximity while for the same scenario the existing system withhardware proximity sensor detects no proximity Because of wrongdetection of the proximity the proposed method has to change in RX rangefrom 26 to 32.

The notation “c” indicates that when hover done to top half of theelectronic device (100). For this scenario range of the RX is 26 to 32and range of the TX is 37 to 41. In result, the proposed solutiondetects proximity while for the same scenario the existing system withhardware proximity sensor detects no proximity Because of the wrongdetection of the proximity the proposed method has to change in RX rangefrom 27 to 29.

The notation “d” indicates that when hover done on the electronic device(100) (i.e., while receiving a call). For this scenario range of the RXis 27 to 29 and range of the TX is 37 to 41. In result, the proposedsolution detects proximity while for the same scenario the existingsystem with hardware proximity sensor detects proximity. Because ofcorrect detection of the proximity the proposed method have to no changein RX and TX range.

The notation “e” indicates that difference in RX and TX value when hoveron top half of the electronic device (100) with 0^(th) hover data (i.e.,obtain hover data at the first time) record. For this example, for RX 320^(th) hover data value is 960 and 1^(st) hover data value is 880.Difference between the 0^(th) and 1^(st) hover is 80.

FIG. 3F illustrates a scenario of fetching hover data and calculatingdifference between column and row electrodes values to determine a firsthover value according to an embodiment of the disclosure.

Referring to FIG. 3F, the notation “a” indicates that reading of touchscreen panel hover data (i.e., RX and TX) when any application of theelectronic device (100) request for proximity sensor data of theelectronic device (100). The notation “b” indicates a threshold valuetable of RXDiff and TXDiff for the different modes of the electronicdevice (100).

The notation “c” indicates that calculation of the first hover valuebased on a different value of the touch screen panel hover data. Thetouch screen panel hover data values are fetched on regular intervals of100 ms. The touch screen panel hover data consist of 47 values (i.e., 32RX value and 15 TX value). The proposed system and method calculateRXDiff and TXDiff value for 50 touch screen panel hover data to detecthover event. For each touch screen panel hover data RXDiff and TXDiff iscalculated from the 0^(th) hover data. The proposed system and methodenable hover if RXDiff and TXDiff is exceed the respective thresholds.

For case-1, calculate the difference between 0^(th) hover data and1^(st) hover data values. In the case-1, the difference between 0^(th)hover data and 1^(st) hover data value is lower than respectivethreshold value. So, the first hover is disabled (i.e., HV1=0). Forcase-2, calculate the difference between 0^(th) hover data and 2^(nd)hover data values. In the case-2, the difference between 0^(th) hoverdata and 2nd hover data value is greater than respective thresholdvalue. So, the first hover is enabled (i.e., HV1=1).

FIG. 4 illustrates a second hover determined by using a trainedprobabilistic TSP hover ML model of the touch panel based on obtainedhover data over a plurality of time intervals according to an embodimentof disclosure.

Referring to FIG. 4, the notation “a” indicates the ML model tocalculate the second hover (i.e., HV2) value. The ML model gives theproximity based on the current RXDiff and TXDiff.

At 402 a, the method includes collecting an array of 48 values of thehover data consisting of 47 hover values (i.e., 32 RX and 15 TX values)and storing the proximity value at 48^(th). At 404 a, the methodincludes normalizing the array and converting into 32×15 matrix. Thenormalizing Equation 1 is given below,

$\begin{matrix}{X^{\prime} = \frac{X - \mu}{\sigma}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

Where μ is mean and σ is a standard deviation.

At 406 a, the method includes predicting the proximity by feeding inputto a model consisting of two convolution (Cony) and one flatten layer(FC layer). The convolutional neural network (CNN) model is meant to berun on the electronic device (100) (e.g., android devices). Therequirement to make a network as shallow as possible. So, the networktakes less time to give output by reducing computations. The proposedsystem and method has only two convolution layers and only one flattenlayer which was giving very high accuracy on the training and testingdataset. Accordingly, there is no need to add unnecessary layers whichdoes not improve the accuracy of the model and increases the totaltraining parameters and matrix operations. At 408 a, the method includestraining the model on around 20K samples. At 410 a, the method includescalculating the probability of the second hover value based on real-timedata. For example, if probability is greater than 0.7 than set HV2=1else HV2=0.

The notation “b” indicates flow of the proposed sequential model. Atstep 402 b-414 b, input is passed through two sets of convolution, batchnormalization, and max pooling layers. The convolution layer is used toextract features set from raw data. The Batch normalization layernormalize the output of the convolution layer which helps layers tolearn more stable distribution of the inputs and increases the trainingspeed. The max pooling layer is used to reduce spatial size of givendata to reduce training parameters and computations. At step 416 b,after second max pooling layer, flatten data converts the input from twodimensional to one dimensional array. At step 418 b-422 b, applies the 1dimensional array to dense layers to predict final output by passingthrough a batch normalization. At step 424 b, softmax activation atoutput layer converts the output into probabilistic distribution.

FIG. 5 is a flow diagram 500A illustrating a method for determining aproximity of at least one object to a touch panel of an electronicdevice based on a first hover and a second hover according to anembodiment of the disclosure.

Referring to FIG. 5, the operations (502 a-510 aa and 502 a-510 ab) areperformed by the proximity determiner (120 c).

At operation 502 a, the method includes assigning the first dynamicweight (i.e., coefficient A) to the first hover. At operation 504 a, themethod includes assigning the second dynamic weight (i.e., coefficientB) to the second hover. At operation 506 a, the method includesdetermining the candidate hover (i.e., HV) based on the first hover, thefirst dynamic weight assigned to the first hover, the second hover, andthe second dynamic weight assigned to the second hover. At operation 508a-510 aa, the method includes detecting one of the at least one objectis in the proximity to the touch panel (140 a) of the electronic device(100) if candidate hover does meet the hover threshold (i.e., 0.65). Atoperation 508 a-510 ab, the method includes detecting the at least oneobject is not in the proximity to the touch panel (140 a) of theelectronic device (100) if candidate hover does not meet the hoverthreshold.

HV=(A*HV1)+(B*HV2)  Equation 2

HV>=0.65, Proximity=1 (in proximity)

HV<0.65, Proximity=0 (not in proximity)

Different models show different TX and RX data. During training the MLModel, the accuracy is determined.

$\begin{matrix}{{Accuracy}{= {1 - \frac{\mspace{14mu} \begin{matrix}{{No}\mspace{14mu} {of}\mspace{14mu} {Incorrect}\mspace{14mu} {Proximity}} \\{{Calculated}\mspace{14mu} {by}\mspace{14mu} {TSP}\mspace{14mu} {Hover}\mspace{14mu} {Process}}\end{matrix}}{{Total}\mspace{14mu} {Records}\mspace{14mu} {Considered}\mspace{14mu} {for}\mspace{14mu} {ML}}}}} & {{Equation}\mspace{14mu} 3}\end{matrix}$

Based on the accuracy the values of coefficients A and B are calculated.Coefficient of TSP hover value (HV1) ‘A’ is calculated as,

A=(1−Accuracy)  Equation 4

Coefficient of TSP hover value of ML model (HV2) ‘B’ is calculated as,

B=Accuracy  Equation 5

If accuracy is less, the TSP Hover process result (HV1) is given moreweightage (A>B). If accuracy is more, the TSP Hover ML Model result(HV2) is given more weightage (B>A).

For example see Table 1

TABLE 1 ML model training (Device A) TSP hover process result correct95500 TSP hover process result incorrect 4500 Total TSP hover dataconsidered 100000

According to Table 1, Accuracy=1−(4500/100000)=0.955, A=1−0.955=0.045,B=0.955=0.955. Since the ML result was more accurate in this case, thecoefficient B>A.

TABLE 2 ML model training (Device B) TSP hover process result correct5500 TSP hover process result incorrect 94500 Total TSP hover dataconsidered 100000

According to Table 2, Accuracy=1−(94500/100000)=0.055, A=1−0.055=0.945,B=0.055=0.055. Since the calculation result was more accurate in thiscase, the coefficient A>B. The variables A and B are electronic device(100) dependent.

FIG. 6 is a flow diagram 600 illustrating a method for determining aproximity for an In call mode and 3^(rd) party application modeaccording to an embodiment of the disclosure.

Referring to FIG. 6, the operations (602-630) are performed by theelectronic device (100).

At 602-610, the method includes registering proximity sensor value forthe proximity sensor when Incall or/and a 3^(rd) party application inthe electronic device (100) needs the proximity sensor value. Virtualproxy service activates if any application registers proximity sensorelse virtual proxy service deactivates. At 612, the method includesdetermining status of call application. The proposed method and systemcalculates the proximity if the call is ongoing.

At 614-630, the proposed method and system calculates the proximity. Theproposed system and method fetching TSP hover data (i.e., the firsthover calculation) values on regular intervals of 100 ms is started. TSPhover data consists of 47 values i.e., 32 RX values and 15 TX values. RXand TX Values are extracted from TSP hover data. RX values are filteredfrom range 27 to 29 and TX values are filtered from range 37 to 41. Theproposed method and system considers threshold of 50 TSP hover records.Experimenting with different scenarios, the proposed method and systemdeduced RX_Threshold=50. TX_Threshold=80. For detecting the proximity bythe proposed method and system already described in FIG. 3C to FIG. 5.

FIG. 7 is a flow diagram 700 illustrating a method for determining aproximity for a pocket mode according to an embodiment of disclosure.

Referring to FIG. 7, the operations (702-720) are performed by theelectronic device (100).

At 702-710, the method includes registering proximity sensor value forthe proximity sensor when the pocket mode initiated in the electronicdevice (100) needs the proximity sensor value. Virtual proxy serviceactivates when pocket mode registers the proximity sensor else virtualproxy services deactivates. At 712, the method includes determiningstatus of screen status of the electronic device (100). The proposedmethod and system calculated the proximity if the screen isactive/awake.

At 714-720, the proposed method and system calculates the proximity. Theproposed system and method fetching TSP hover data (i.e., the firsthover calculation) values on regular intervals of 100 ms is started. TSPhover data consists of 47 values i.e., 32 RX values and 15 TX values. RXand TX Values are extracted from TSP hover data. RX values are filteredfrom range 26 to 30 and TX values are filtered from range 37 to 41. Theproposed method and system considers threshold of 50 TSP hover records.Experimenting with different scenarios, the proposed method and systemdeduced RX_Threshold=50. TX_Threshold=70. For detecting the proximity bythe proposed method and system already described in FIG. 3C to FIG. 5.

FIGS. 8A and 8B illustrate a proximity determination when a call isreceived and an electronic device is far/near from a user of anelectronic device according to various embodiments of the disclosure.

Referring to FIGS. 8A and 8B, the notation “a” indicates that when thecall is received and the electronic device (100) is far from the user ofthe electronic device (100). Since the electronic device (100) is far,the TSP hover result in the RXDiff<RX_Threshold and TXDiff<TX_Thresholdas shown in the heat map the greater difference turned to greater darkmap. The value of the HV1 is set to be 0. In addition, when calculatingthe hover value from trained ML Model, the value of the HV2 is set to be0. Thus the HV (final hover value) is 0, means no proximity and screenwill turn on.

The notation “b” indicates that when the call is received and theelectronic device (100) is near to the user of the electronic device(100). Since the electronic device (100) is near, the TSP hover resultin the RXDiff>RX_Threshold and TXDiff>TX_Threshold as shown in the heatmap the greater difference turned to greater dark map. The value of theHV1 is set to be 1. In addition, when calculating the hover value fromtrained ML Model, the value of the HV2 is set to be 1. Thus the HV(final hover value) is greater than 0.65, means proximity and screenwill turn off.

FIGS. 9A and 9B illustrate proximity determination when a hover isperformed on second/first half of an electronic device according tovarious embodiments of the disclosure.

Referring to FIGS. 9A and 9B, the notation “a” indicates that when hoveris done on second half or motion is shown in arrow below dotted line.Since the hover is far, the TSP hover result in the RXDiff<RX_Thresholdand TXDiff<TX_Threshold as shown in the heat map the greater differenceturned to greater dark map. The HV1value is set to 0. In addition, whencalculating the hover value from trained ML Model, the value of the HV2is set to be 0. Thus the HV (final hover value) is 0, means no proximityand screen will turn on.

The notation “b” indicates that when hover is done on first half ormotion is shown in arrow below dotted line. Since the hover is far, theTSP hover result in the RXDiff>RX_Threshold and TXDiff>TX_Threshold asshown in the heat map the greater difference turned to greater dark map.The HV1value is set to 1. In addition, when calculating the hover valuefrom trained ML Model, the value of the HV2 is set to be 1. Thus the HV(final hover value) is greater than 0.65, means proximity and screenwill turn off.

The embodiments disclosed herein can be implemented using at least onesoftware program running on at least one hardware device and performingnetwork management functions to control the elements.

The foregoing description of the specific embodiments will so fullyreveal the general nature of the embodiments herein that others can, byapplying current knowledge, readily modify and/or adapt for variousapplications such specific embodiments without departing from thegeneric concept, and, therefore, such adaptations and modificationsshould and are intended to be comprehended within the meaning and rangeof equivalents of the disclosed embodiments. It is to be understood thatthe phraseology or terminology employed herein is for the purpose ofdescription and not of limitation.

While the disclosure has been shown and described with reference tovarious embodiments thereof, it will be understood by those skilled inthe art that various changes in form and details may be made thereinwithout departing from the spirit and scope of the disclosure as definedby the appended claims and their equivalents.

What is claimed is:
 1. A method for determining a proximity of at leastone object using an electronic device, the method comprising: obtaining,by the electronic device, hover data from a touch panel of theelectronic device; determining, by the electronic device, at least oneof a first hover or a second hover based on the hover data of the atleast one object obtained for the touch panel; and determining, by theelectronic device, the proximity of the at least one object to the touchpanel of the electronic device based on at least one of the first hoveror the second hover.
 2. The method of claim 1, wherein the first hoveris a touch panel calculation-based hover and the second hover is machinelearning (ML)-based hover.
 3. The method of claim 1, wherein the hoverdata is obtained based on at least one sensor, and wherein the at leastone sensor includes one of a capacitive sensor, a resistive sensor, aninductive sensor, an ultrasonic sensor, or a luminance sensor.
 4. Themethod of claim 3, wherein the hover data is obtained based on a changein capacitance of column and row electrodes of the touch panel while thehover of the at least one object is detected over the touch panel of theelectronic device.
 5. The method of claim 4, wherein the column and rowelectrodes of the touch panel are automatically configured by:detecting, by the electronic device, at least one application running inthe electronic device; determining, by the electronic device, the columnand row electrodes of a row-and-column electrode matrix of the touchscreen based on the at least one application running in the electronicdevice; and automatically, by the electronic device, configure thecolumn and row electrodes to obtain the hover data of the at least oneobject.
 6. The method of claim 1, wherein the determining of the firsthover based on the hover data of the at least one object obtained forthe touch panel comprises: obtaining, by the electronic device, thehover data at a first time; obtaining, by the electronic device, thehover data at a second time; determining, by the electronic device, ahover data difference between the hover data obtained at the first timewith the hover data obtained at the second time; determining, by theelectronic device, whether the hover data difference meets a capacitancethreshold; and determining, by the electronic device, the first hoverwhen the hover data difference meets the capacitance threshold.
 7. Themethod of claim 1, wherein the second hover is determined by using atrained probabilistic touch screen panel (TSP) Hover ML model of thetouch panel based on the obtained the hover data over a plurality oftime intervals.
 8. The method of claim 1, wherein the determining of theproximity of the at least one object to the touch panel of theelectronic device based on the first hover and the second hovercomprises: assigning, by the electronic device, a first dynamic weightto the first hover; assigning, by the electronic device, a seconddynamic weight to the second hover; determining, by the electronicdevice, a candidate hover based on the first hover, the first dynamicweight assigned to the first hover, the second hover, and the seconddynamic weight assigned to the second hover; determining, by theelectronic device, whether the candidate hover meets a hover threshold;and detecting, by the electronic device, one of the at least one objectis in the proximity to the touch panel of the electronic device when thecandidate hover meets the hover threshold and the at least one object isnot in the proximity to the touch panel of the electronic device whenthe candidate hover does not meets the hover threshold.
 9. An electronicdevice for determining a proximity of at least one object using anelectronic device, the electronic device comprising: a memory; at leastone processor, operationally coupled to the memory, the at least oneprocessor configured to: obtain hover data from a touch panel of theelectronic device, determine at least one of a first hover or a secondhover based on the hover data of the at least one object obtained forthe touch panel, and determine the proximity of the at least one objectto the touch panel of the electronic device based on at least one of thefirst hover or the second hover.
 10. The electronic device of claim 9,wherein the first hover includes a touch panel calculation-based hoverand the second hover is a machine learning (ML)-based hover.
 11. Theelectronic device of claim 9, wherein the hover data is obtained basedon at least one sensor, and wherein the at least one sensor includes oneof a capacitive sensor, a resistive sensor, an inductive sensor, anultrasonic sensor, or a luminance sensor.
 12. The electronic device ofclaim 11, wherein the hover data is obtained based on a change incapacitance of column and row electrodes of the touch panel while thehover of the at least one object is detected over the touch panel of theelectronic device.
 13. The electronic device of claim 10, wherein the atleast one processor is further configured to automatically configure thecolumn and row electrodes of the touch panel, and the at least oneprocessor being further configured to: detect at least one applicationrunning in the electronic device; determine, the column and rowelectrodes of a row-and-column electrode matrix of the touch screenbased on the at least one application running in the electronic device;and automatically, configure the column and row electrodes to obtain thehover data of the at least one object.
 14. The electronic device ofclaim 9, wherein the at least one processor is further configured to:obtain the hover data at a first time; obtain the hover data at a secondtime; determine a hover data difference between the hover data obtainedat the first time with the hover data obtained at the second time;determine whether the hover data difference meets a capacitancethreshold; and determine the first hover when the hover data differencemeets the capacitance threshold.
 15. The electronic device of claim 9,wherein the at least one processor is further configured to determinethe second hover by using a trained probabilistic touch screen panel(TSP) Hover ML model of the touch panel based on the obtained the hoverdata over a plurality of time intervals.
 16. The electronic device ofclaim 9, wherein the at least one processor is further configured todetermine the proximity of the at least one object to the touch panel ofthe electronic device based on the first hover and the second hover, andthe at least one processor being further configured to: assign a firstdynamic weight to the first hover; assign a second dynamic weight to thesecond hover; determine a candidate hover based on the first hover, thefirst dynamic weight assigned to the first hover, the second hover, andthe second dynamic weight assigned to the second hover; determinewhether the candidate hover meets a hover threshold; and detect one ofthe at least one object is in the proximity to the touch panel of theelectronic device when the candidate hover meets the hover threshold andthe at least one object is not in the proximity to the touch panel ofthe electronic device when the candidate hover does not meet the hoverthreshold.
 17. The method of claim 4, further comprising: setting arange and threshold difference value of the column and row electrodes ofthe touch panel based on proximity sensor data.
 18. The method of claim7, further comprising: calculating the probability of the second hovervalue based on real-time data.